Global Nerdy – Joey deVilla's mobile/tech bloghttp://www.globalnerdy.com
Computer-y, with a chance of mobileMon, 19 Nov 2018 20:58:23 +0000en-UShourly1https://wordpress.org/?v=4.9.8I try my best NOT to be THIS type of project managerhttp://www.globalnerdy.com/2018/11/19/try-best-not-type-project-manager/
http://www.globalnerdy.com/2018/11/19/try-best-not-type-project-manager/#respondMon, 19 Nov 2018 20:58:23 +0000http://www.globalnerdy.com/?p=26250The post I try my best NOT to be THIS type of project manager appeared first on Global Nerdy - Joey deVilla's mobile/tech blog.
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]]>http://www.globalnerdy.com/2018/11/17/whats-happening-tampa-bay-tech-entrepreneur-nerd-scene-week-monday-november-19-2018/feed/0How to win Startup Weekend Tampa Bay 2018http://www.globalnerdy.com/2018/11/16/win-startup-weekend-tampa-bay-2018/
http://www.globalnerdy.com/2018/11/16/win-startup-weekend-tampa-bay-2018/#respondFri, 16 Nov 2018 15:26:18 +0000http://www.globalnerdy.com/?p=26235This weekend marks the end of Global Entrepreneurship Week Tampa Bay, and it’s ending with a bang: the Tampa edition of Startup Weekend! It’s a 54-hour whirlwind event where you get to be part of building a business in a weekend, Silicon Valley style. It’s about pitching, business model creation, prototyping, designing and market validation […]

This weekend marks the end of Global Entrepreneurship Week Tampa Bay, and it’s ending with a bang: the Tampa edition of Startup Weekend! It’s a 54-hour whirlwind event where you get to be part of building a business in a weekend, Silicon Valley style. It’s about pitching, business model creation, prototyping, designing and market validation all in a friendly and supportive team environment, and in Factory 114’s beautiful coworking space.

If you’re participating in Startup Weekend, you’re probably wondering how to maximize your chances of winning. If so, this article’s for you!

Designate a project manager from the get-go and define your methods of communication. Pick someone to coordinate the effort, and stick to the simplest possible communication tool that everyone on the team is familiar with.

Work backwards from the end product. Hackathons are chaotic events often held in noisy spaces, and it’s way too easy to get sidetracked, go down technical rabbit holes, or just talk over each other. Keep asking “What’s the goal? What do we want at the end of these 48+ hours?”

Divide and conquer. Come up with a rough idea of the solution and how you’ll get to it, divide the problem into smaller pieces, and split your team into pieces by skill set to tackle those problems.

Validate, validate, validate. Not just your code, but your product idea. Talk to potential customers, either in person, or online!

Check in with your teammates. “Sure, this is touchy-feely and didn’t contribute to hard development, but it helped us become a unified team.”

Take advantage of free advice, but be specific in your ask. Startup Weekend-style events often provide industry experts, and you should make use of them — that’s what they’re there for! Just be aware that they have to support other teams, so be sure to have questions on specific topics for them, and not just general hand-wavey ones.

Show your team you trust them. Don’t micromanage. “Take a leap of faith and let them take a stab at delivering. Then give them your feedback.”

Laugh. Have fun. Sometimes, this is the easiest thing to forget.

Be authentic. “The final pitches that grabbed my attention (and stopped me from glancing at the #swyyc Twitter feed), were the ones where the founder was clearly passionate about the idea and it showed in their eyes.”

Take care of your body: Hydrate. Eat well. And sleep, somewhat. “Don’t just stock up on the sugary treats and salty snacks. Eat lots of fruit, veggies, and get your protein.” Also, a little sleep can go a long way — make sure you get some!

Have a rough idea of your business model. “If you’re going to be pitching an idea, then you should have a basic understanding of how that idea can be turned into a real business.”

Do your research. Who are your competitors? What strategies are similar companies using? Who are your key influencers?

Get to know your fellow participants. You’ll need to do this in order to build your team.

Build your team by selling yourself and your idea. Your pitch format should be along these lines: “Hi, My name is _____ and I’m here today to invite you to join me in <business name> that <main value prop>. My background is in <relevant background> and I’ve seen a problem/opportunity where <problem/opportunity>. I think we can solve this by <how you do it>. I need <mobile app developer / web developer / UX designer /etc.>

Set Friday night goals. 1. Quickly do introductions amongst your team, and sort out roles. 2. Quickly revise your business model canvas, with new input from your team. 3. Register a domain and setup a landing page with an e-mail capture. 4. Work out what your MVP is going to look like.

Saturday: Build it and sell it.

Sunday: Pitch! Focus on 3 things: Is it pretty? Does it work? Can you sell it?

]]>http://www.globalnerdy.com/2018/11/16/win-startup-weekend-tampa-bay-2018/feed/0Tampa Bay’s tech “scenius” depends on ushttp://www.globalnerdy.com/2018/11/15/tampa-bays-tech-scenius-depends-on-us/
http://www.globalnerdy.com/2018/11/15/tampa-bays-tech-scenius-depends-on-us/#respondThu, 15 Nov 2018 15:20:16 +0000http://www.globalnerdy.com/?p=26183Here’s a word that you should add to your vocabulary: Scenius. It was coined by musician, music producer, and visual artist Brian Eno to describe the extreme creativity that groups, places, or “scenes” can generate. Eno came up with the term as a way of countering the pervasive myth of the Lone Genius — the idea […]

Here’s a word that you should add to your vocabulary: Scenius. It was coined by musician, music producer, and visual artist Brian Eno to describe the extreme creativity that groups, places, or “scenes” can generate.

Eno came up with the term as a way of countering the pervasivemythoftheLoneGenius — the idea that innovation comes from a small, select set of Chosen Ones.

Here’s an expanded definition, courtesy of Eno:

“Scenius stands for the intelligence and the intuition of a whole cultural scene. It is the communal form of the concept of the genius.”

…I thought that originally those few individuals who’d survived in history – in the sort-of “Great Man” theory of history – they were called “geniuses”. But what I thought was interesting was the fact that they all came out of a scene that was very fertile and very intelligent.

So I came up with this word “scenius” – and scenius is the intelligence of a whole… operation or group of people. And I think that’s a more useful way to think about culture, actually. I think that – let’s forget the idea of “genius” for a little while, let’s think about the whole ecology of ideas that give rise to good new thoughts and good new work.”

Here are some examples of scenius, where the collective smarts, creativity, and passion of a group of people coming together to do great things is greater than the sum of its parts:

The Lunar Society of Birmingham: a dinner club run between 1765 and 1813 in Birmingham, England, and attended by industrialists, scientists, and thinkers who changed science and engineering forever. Their regulars included Boulton and Watt (steam engines and their applications to manufacturing), Erasmus Darwin (biology, inventions, and grandfather of Charles Darwin), Keir (industrialist, chemistry, inventions), Priestly (chemistry, philosophy), Small (Thomas Jefferson’s professor at the College of William and Mary), Stokes and Withering (early heart medicine), Wedgewood (industrialized pottery, pretty much invented modern marketing, including the concepts of direct mail, money-back guarantees, self-service, free delivery, buy one get one free, and illustrated catalogs), Whitehurst (geology).

The Sex Pistols’ 1976 gig. It was attended by a mere 42 people, but those people went on to revolutionize music through their work in British alt-rock: Howard Devoto and Pete Shelley (The Buzzcocks) organized, Tom Wilson and Martin Hannett (Factory Records and the Hacienda), Morrissey, Mark E. Smith (The Fall), Paul Morley (NME magazine, Frankie Goes to Hollywood, The Art of Noise), Mick Hucknall (Simply Red), and Ian Curtis, Bernard Sumner, and Peter Hook (Joy Division, New Order).

Silicon Valley: Your iPhone and Android are direct descendants of the scenius that was born when the “Traitorous Eight” left Shockley Semiconductor to form their own company, Fairchild, and the “Fairchildren” who then left Fairchild to form their own companies, and so on, creating a cross-pollenating scene that we now know as “The Valley”.

Mutual appreciation — Risky moves are applauded by the group, subtlety is appreciated, and friendly competition goads the shy. Scenius can be thought of as the best of peer pressure.

Rapid exchange of tools and techniques — As soon as something is invented, it is flaunted and then shared. Ideas flow quickly because they are flowing inside a common language and sensibility.

Network effects of success — When a record is broken, a hit happens, or breakthrough erupts, the success is claimed by the entire scene. This empowers the scene to further success.

Local tolerance for the novelties — The local “outside” does not push back too hard against the transgressions of the scene. The renegades and mavericks are protected by this buffer zone.

BarCamp Tampa Bay, November 10, 2018.

Here’s what Austin Kleon, a writer and artist whose ideas have been adopted by the tech community, has to say about scenius:

Under this model, great ideas are often birthed by a group of creative individuals—artists, curators, thinkers, theorists, and other tastemakers—who make up an “ecology of talent.” If you look back closely at history, many of the people who we think of as lone geniuses were actually part of “a whole scene of people who were supporting each other, looking at each other’s work, copying from each other, stealing ideas, and contributing ideas.” Scenius doesn’t take away from the achievements of those great individuals: it just acknowledges that good work isn’t created in a vacuum, and that creativity is always, in some sense, a collaboration, the result of a mind connected to other minds.

What I love about the idea of scenius is that it makes room in the story of creativity for the rest of us: the people who don’t consider ourselves geniuses. Being a valuable part of a scenius is not necessarily about how smart or talented you are, but about what you have to contribute—the ideas you share, the quality of the connections you make, and the conversations you start. If we forget about genius and think more about how we can nurture and contribute to a scenius, we can adjust our own expectations and the expectations of the worlds we want to accept us. We can stop asking what others can do for us, and start asking what we can do for others.

Tampa Community Connect, October 20, 2018.

The time is ripe to build Tampa Bay’s tech scenius. Consider these recent developments in the area…

Over the past little while, the elements of an interesting, vital technology scene have been gathering in Tampa Bay and the surrounding area (I like to think of Tampa Bay as the western end of the “Orlampa” corridor, with Orlando — who have a lively tech community of their own — at the eastern end). This scene will be boosted by the arrival of that startup hub and gathering place Embarc Collective in March:

Click the image to see it at full size.

Click the image to see it at full size.

Click the image to see it at full size.

While the elements of scenius are in place for Tampa Bay’s tech scene, there’s still some way to go before Tampa can match places like Nashville — whose tech scene is biggerthanyoumightthink — never mind places like Austin, Charlotte, Indianapolis, and Raleigh.

The success or failure of Tampa’s tech scenius depends on us, the Tampeños who work in tech, creative, and related industries.

I’m originally from Toronto. While it has one of the hottesttechscenesinNorthAmericatoday, it wasn’t that way 15 years ago. While the city did launch some initiatives to change this, what truly made the difference was Toronto’s own tech community stepping up and organizing. We held events of all sizes, from regular meetups and user group meetings at pubs and lecture halls to independent conferences like Mesh, RubyFringe and FutureRuby to tech “camp” events to big corporate gatherings put on by the likes of the Canadian subsidiaries of IBM and Microsoft. We built places to get together, from hackerspaces such as Hacklab and Site3 coLaboratory to the MaRS Centre. In my work as a developer evangelist for Microsoft, I’ve met many students at Toronto’s fine universities and colleges, and they’re eager to crank out the ‘wares, both hard and soft, and they’re bright as all get-out. We built a great community bound together by cooperation, a strong social media scene and good old-fashioned face-to-face meetings. We got stuff done, and the stuff we did traveled far and wide. We built Toronto’s tech scenius, and it put the city on the map.

]]>http://www.globalnerdy.com/2018/11/15/tampa-bays-tech-scenius-depends-on-us/feed/0What’s happening in the Tampa Bay tech/entrepreneur/nerd scene (Week of Monday, November 12, 2018)http://www.globalnerdy.com/2018/11/11/whats-happening-tampa-bay-tech-entrepreneur-nerd-scene-week-monday-november-12-2018/
http://www.globalnerdy.com/2018/11/11/whats-happening-tampa-bay-tech-entrepreneur-nerd-scene-week-monday-november-12-2018/#respondSun, 11 Nov 2018 04:24:15 +0000http://www.globalnerdy.com/?p=26177The scene at BarCamp Tampa Bay 2018 last Saturday. Every week, I compile a list of events for developers, technologists, tech entrepreneurs, and nerds in and around the Tampa Bay area. We’ve got a lot of events going on this week, and here they are! Monday, November 12 Geekocracy! — Disc Golf at 22nd St. […]

]]>http://www.globalnerdy.com/2018/11/11/whats-happening-tampa-bay-tech-entrepreneur-nerd-scene-week-monday-november-12-2018/feed/0Data science reading list for Wednesday, November 7, 2018: The job — working together to build trust, the kinds of data scientist, why mothers should do data science, and why not to be a generalisthttp://www.globalnerdy.com/2018/11/07/data-science-reading-list-wednesday-november-7-2018-job-working-together-build-trust-kinds-data-scientist-mothers-data-science-not-gener/
http://www.globalnerdy.com/2018/11/07/data-science-reading-list-wednesday-november-7-2018-job-working-together-build-trust-kinds-data-scientist-mothers-data-science-not-gener/#respondWed, 07 Nov 2018 16:43:38 +0000http://www.globalnerdy.com/?p=26160
Your #datascience reading list for the day: building trust in data science by working together, the kinds of data scientist, why moms should be data scientists, and why not to be a data science generalist.

As data science systems become more widespread, effectively governing and managing them has become a top priority for practitioners and researchers. While data science allows researchers to chart new frontiers, it requires varied forms of discretion and interpretation to ensure its credibility. Central to this is the notion of trust – how do we reliably know the trustworthiness of data, algorithms and models?

In 2012, HBR dubbed data scientist “the sexiest job of the 21st century”. It is also, arguably, the vaguest. To hire the right people for the right roles, it’s important to distinguish between different types of data scientist. There are plenty of different distinctions that one can draw, of course, and any attempt to group data scientists into different buckets is by necessity an oversimplification. Nonetheless, I find it helpful to distinguish between the deliverables they create. One type of data scientist creates output for humans to consume, in the form of product and strategy recommendations. They are decision scientists. The other creates output for machines to consume like models, training data, and algorithms. They are modeling scientists.

For several women, the time during their pregnancy is one of overwhelming happiness, and at times, worry. We worry about things like childbirth and not knowing what to do with our baby after he or she is born. Women with careers have an added worry; we think about how this adorable new addition to our family will impact our careers.

One thing that I’ve discovered over the past four years is that having certain skills can reduce uncertainty around our careers. I’m a mom of two little girls and have a career in data that has provided me with the more flexibility and less stress. Below, I outline the three reasons why mothers should consider a career in data science.

I work at a data science mentorship startup, and I’ve found there’s a single piece of advice that I catch myself giving over and over again to aspiring mentees. And it’s really not what I would have expected it to be.

Rather than suggesting a new library or tool, or some resume hack, I find myself recommending that they first think about what kind of data scientist they want to be.

The reason this is crucial is that data science isn’t a single, well-defined field, and companies don’t hire generic, jack-of-all-trades “data scientists”, but rather individuals with very specialized skill sets.

To see why, just imagine that you’re a company trying to hire a data scientist. You almost certainly have a fairly well-defined problem in mind that you need help with, and that problem is going to require some fairly specific technical know-how and subject matter expertise. For example, some companies apply simple models to large datasets, some apply complex models to small ones, some need to train their models on the fly, and some don’t use (conventional) models at all.

Each of these calls for a completely different skill set, so it’s especially odd that the advice that aspiring data scientists receive tends to be so generic: “learn how to use Python, build some classification/regression/clustering projects, and start applying for jobs.”

]]>http://www.globalnerdy.com/2018/11/07/data-science-reading-list-wednesday-november-7-2018-job-working-together-build-trust-kinds-data-scientist-mothers-data-science-not-gener/feed/0A new mantra worth consideringhttp://www.globalnerdy.com/2018/11/06/new-mantra-worth-considering/
http://www.globalnerdy.com/2018/11/06/new-mantra-worth-considering/#respondTue, 06 Nov 2018 15:30:59 +0000http://www.globalnerdy.com/?p=26151Click the image to see it at full size.

]]>http://www.globalnerdy.com/2018/11/04/whats-happening-tampa-bay-tech-entrepreneur-nerd-scene-week-monday-november-5-2018/feed/0Data science reading list for Friday, November 2, 2018: Education, aspirations, and job descriptionshttp://www.globalnerdy.com/2018/11/02/data-science-reading-list-friday-november-2-2018-education-aspirations-job-descriptions/
http://www.globalnerdy.com/2018/11/02/data-science-reading-list-friday-november-2-2018-education-aspirations-job-descriptions/#respondFri, 02 Nov 2018 13:19:39 +0000http://www.globalnerdy.com/?p=26124With Student Interest Soaring, Berkeley Creates New Data-Sciences Division From Chronicle of Higher Education: Berkeley’s move follows MIT’s announcement last month that it was investing $1 billion in a new college of artificial intelligence. But leaders at Berkeley say their disclosure of the division today was driven by an imminent international search for a director, who will […]

Berkeley’s move follows MIT’s announcement last month that it was investing $1 billion in a new college of artificial intelligence. But leaders at Berkeley say their disclosure of the division today was driven by an imminent international search for a director, who will hold the title of associate provost, putting the program on an institutional par with Berkeley’s colleges and schools. They explain that in creating a division rather than a new college, they are reflecting the way data science has become woven into every discipline.

Berkeley has been planning the division for four years, said David Culler, interim dean for data sciences, and has been rolling it out incrementally through a new data-sciences major approved last year, and corresponding growth in data-science courses. Enrollment in “Foundations of Data Science” has soared from 100 in 2015 to 1,300 in 2018. Enrollment in the upper-level “Principles and Techniques of Data Science” has grown from 100 in 2016 to 800 students. The emerging program has served as a “pilot” for the division, which is now set to evolve under a new director.

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The core of the data-science curriculum, said Culler, is computer science and statistics, with additional depth courses in optimization and visualization. But students will also be required to have a “domain emphasis” that would most likely synthesize material from various other departments. For instance, a data-science student’s exploration of social inequality might include courses in sociology, ethnic studies, economics, and philosophy.

Next week at the National Analytics Conference, [Jennifer Cruise from the Aon Centre for Innovation and Analytics] will be on a panel where she expects to discuss several aspects and challenges that businesses face relating to data, including how to deal with the abundance of information that is now available and, of course, the key issues of skills and resources.

“You can only truly exploit the data if you get the right people in that space, and there’s a double whammy,” she said. “On the one hand, you have a lack of hands-on resources. Skilled data scientists are hard to come by and things are changing quickly, so people who are qualified need to stay on top of things. Then, you also have a gap in the leadership space – the people who can advise you how to turn [data] into revenue for your company, or how to use your data to become more operationally efficient.”

So, you want to be become a data scientist? Great. But you have zero experience and have no clue how to get started in this field. I get it. I’ve been there and I definitely feel you. This is why this post is for you.

All the questions below came from the community through my LinkedIn post, email, and other channels. I hope that by sharing my experience, you will be enlightened on how to pursue a data science career and make your learning journey fun.

In today’s data science job market, demand far outstrips supply, said Chris Nicholson, co-founder and CEO of artificial intelligence and deep learning company Skymind, and co-creator of the open source framework Deeplearning4j. That means organizations must resist the temptation to seek candidates with every last required data science skill in favor of hiring for potential and then training on the job, he said.

“A lot of data science has to do with statistics, math and experimentation—so you’re not necessarily looking for someone with a computer science or software engineering background, though they should have some programming experience,” Nicholson said. “You want folks from physical science, math, physics, natural sciences backgrounds; people who are trained to think about statistical ideas and use computational tools. They need to have the ability to look at data and use tools to manipulate it, explore correlations and produce data models that make predictions.”

Because a data scientist’s job isn’t to engineer entire systems, minimal programming experience is fine, Nicholson said. After all, most organizations can rely on software engineering, DevOps, or IT teams to build, manage and maintain infrastructure in support of data science efforts. Instead, strong data science candidates often have a background in science and should be proficient with data science tools in one or more different stacks.

]]>http://www.globalnerdy.com/2018/11/02/data-science-reading-list-friday-november-2-2018-education-aspirations-job-descriptions/feed/0Data science reading list for Thursday, November 1, 2018: Free data science books for beginners with limited budgetshttp://www.globalnerdy.com/2018/11/01/data-science-reading-list-thursday-november-1-2018-free-data-science-books-beginners-limited-budgets/
http://www.globalnerdy.com/2018/11/01/data-science-reading-list-thursday-november-1-2018-free-data-science-books-beginners-limited-budgets/#respondThu, 01 Nov 2018 15:05:51 +0000http://www.globalnerdy.com/?p=26108If you want to get into data science with a limited budget, this reading list is for you — it’s all about data science and related books that you can get for free! Allen B. Downey’s free Python and math books Allen B. Downey is a believer in free books, and has a whole […]

Allen B. Downey’s free Python and math books

A free book is the root of a tree of potential adaptations, translations, and entirely new books that branch out from the original. Free books transform readers into proof-readers, editors, anthologists, correspondents, contributors, collaborators, writers and authors.

If you are thinking about writing a book, start soon, release early and often, give up control but do a little policing, keep a contributor list, and make it free.

He’s written a number of free books, and the ones most applicable to data science are:

Bayesian Methods for Hackers is described as “an intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view”, and its key chapters are available online, for free, in Jupyter notebook form. The method for reading it that the authors recommend is to clone the book’s Jupyter notebook repo and run it on your local machine.

Another Python/data science book in Jupyter notebook form! This one assumes that you’re familiar with Python, as it’s all about the libraries that are most used for data science and machine learning: NumPy, Pandas, Matplotlib, and Scikit-Learn.

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science.

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This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.

This book writes down the process of data analysis with a minimum of technical detail. What we describe is not a specific “formula” for data analysis, but rather is a general process that can be applied in a variety of situations. Through our extensive experience both managing data analysts and conducting our own data analyses, we have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of our experience in a format that is applicable to both practitioners and managers in data science.

]]>http://www.globalnerdy.com/2018/11/01/data-science-reading-list-thursday-november-1-2018-free-data-science-books-beginners-limited-budgets/feed/0Jupyter risinghttp://www.globalnerdy.com/2018/10/31/jupyter-rising/
http://www.globalnerdy.com/2018/10/31/jupyter-rising/#respondWed, 31 Oct 2018 17:07:54 +0000http://www.globalnerdy.com/?p=26092First of all, if you’re interested in a one-day conference that also gets you a chance to enjoy Florida’s warm winter and Disney World as well, check out DevFest Florida 2019. It takes place on Saturday, January 19, 2019, and I’ll be giving the Jumping into Jupyter Notebooks presentation, which will largely be a hands-on […]

Jupyter is a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text and multimedia resources in a single document. Computational notebooks have been around for decades, but Jupyter in particular has exploded in popularity over the past couple of years. This rapid uptake has been aided by an enthusiastic community of user–developers and a redesigned architecture that allows the notebook to speak dozens of programming languages — a fact reflected in its name, which was inspired, according to co-founder Fernando Pérez, by the programming languages Julia (Ju), Python (Py) and R.

You may want to think of Jupyter notebooks as a wiki with a REPL. Its contents are divided into cells, which contain either:

Narrative content, which you enter in Markdown, and

Code — and if it runs, its output — which you can enter in Python or nearly four dozen other programming languages.

Computational notebooks are essentially laboratory notebooks for scientific computing. Instead of pasting, say, DNA gels alongside lab protocols, researchers embed code, data and text to document their computational methods. The result, says Jupyter co-creator Brian Granger at California Polytechnic State University in San Luis Obispo, is a “computational narrative” — a document that allows researchers to supplement their code and data with analysis, hypotheses and conjecture.

For data scientists, that format can drive exploration. Notebooks, Barba says, are a form of interactive computing, an environment in which users execute code, see what happens, modify and repeat in a kind of iterative conversation between researcher and data. They aren’t the only forum for such conversations — IPython, the interactive Python interpreter on which Jupyter’s predecessor, IPython Notebook, was built, is another. But notebooks allow users to document those conversations, building “more powerful connections between topics, theories, data and results”, Barba says.

Researchers can also use notebooks to create tutorials or interactive manuals for their software. This is what Mackenzie Mathis, a systems neuroscientist at Harvard University in Cambridge, Massachusetts, did for DeepLabCut, a programming library her team developed for behavioural-neuroscience research. And they can use notebooks to prepare manuscripts, or as teaching aids. Barba, who has implemented notebooks in every course she has taught since 2013, related at a keynote address in 2014 that notebooks allow her students to interactively engage with — and absorb material from — lessons in a way that lectures cannot match. “IPython notebooks are really a killer app for teaching computing in science and engineering,” she said.

Ed. note: Before they were called Jupyter notebooks, they were called IPython notebooks.

]]>http://www.globalnerdy.com/2018/10/31/jupyter-rising/feed/0Data science reading list for Wednesday, October 31, 2018: Actually, it’s about ethics in data sciencehttp://www.globalnerdy.com/2018/10/31/data-science-reading-list-wednesday-october-31-2018-actually-ethics-data-science/
http://www.globalnerdy.com/2018/10/31/data-science-reading-list-wednesday-october-31-2018-actually-ethics-data-science/#respondWed, 31 Oct 2018 13:32:38 +0000http://www.globalnerdy.com/?p=26081DJ Patil’s code of ethics for data science From the article: 2.5 quintillion bytes of data are created every day. It’s created by you when you’re commute to work or school, when you’re shopping, when you get a medical treatment, and even when you’re sleeping. It’s created by you, your neighbors, and everyone around you. […]

2.5 quintillion bytes of data are created every day. It’s created by you when you’re commute to work or school, when you’re shopping, when you get a medical treatment, and even when you’re sleeping. It’s created by you, your neighbors, and everyone around you. So, how do we ensure it’s used ethically?

Back in 2014, before I entered public service, I wrote a post called Making the World Better One Scientist at a Time that discussed concerns I had at the time about data. What’s interesting, is how much of it is still relevant today. The biggest difference? The scale of data and coverage of data has massively increased since then and with it the opportunity to do both good and bad.

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With the old adage that with great power comes great responsibility, it’s time for the data science community to take a leadership role in defining right from wrong. Much like the Hippocratic Oath defines Do No Harm for the medical profession, the data science community must have a set of principles to guide and hold each other accountable as data science professionals. To collectively understand the difference between helpful and harmful. To guide and push each other in putting responsible behaviors into practice. And to help empower the masses rather than to disenfranchise them. Data is such an incredible lever arm for change, we need to make sure that the change that is coming, is the one we all want to see.

So how do we do it? First, there is no single voice that determines these choices. This MUST be community effort. Data Science is a team sport and we’ve got to decide what kind of team we want to be.

THE TECH INDUSTRY is having a moment of reflection. Even Mark Zuckerberg and Tim Cook are talking openly about the downsides of software and algorithms mediating our lives. And while calls for regulation have been met with increased lobbying to block or shape any rules, some people around the industry are entertaining forms of self regulation. One idea swirling around: Should the programmers and data scientists massaging our data sign a kind of digital Hippocratic oath?

Microsoft released a 151-page book last month on the effects of artificial intelligence on society that argued “it could make sense” to bind coders to a pledge like that taken by physicians to “first do no harm.” In San Francisco Tuesday, dozens of data scientists from tech companies, governments, and nonprofits gathered to start drafting an ethics code for their profession.

“We have to empower the people working on technology to say ‘Hold on, this isn’t right,’” DJ Patil, chief data scientist for the United States under President Obama, told WIRED. (His former White House post is currently vacant.) Patil kicked off the event, called Data For Good Exchange. The attendee list included employees of Microsoft, Pinterest, and Google.

Schaun Wheeler’s take on codes of data ethics: Not just unimplementable, but built on the wrong foundation

I’m still making up my mind about Schaun Wheeler’s contrarian take on codes of data ethics, but that may be colored by my dislike of Joel Grus’ dickishly libertarian “fuck your ethics” stance. I’ve included Wheeler’s take, along with his interview on Joel Grus’ podcast, Adversarial Learning, for the sake of completeness, with the caveat that I’m undecided on it.

dj patilrecently wrote about the need for a code of ethics for data science. It’s not clear to me that data science as a profession is ready for a code of ethics. Codes are just words unless there is a mechanism to enforce sanctions against people who disregard those codes, and I’m pretty sure no single data science community is cohesive enough to enforce rules even for its own members.

Last week, I wrote about my skepticism of Data for Democracy’s intent to create a data science code of ethics. My concerns focused on the practical feasibility of the project. After a lot of talking about, reading, and watching the evolution of the D4D code of ethics, I still believe the proposed principles are largely unactionable. I also believe, now, that what the working groups have produced is built on the wrong foundation entirely. This isn’t about iterating forward to a solution. No amount of revision can succeed if you’re building the wrong thing.

We need to be clear on what a code of ethics means. If we can realistically expect everyone in the community to just adopt a code of ethics because they intuitively feel that it’s the good and right thing to do, then the code of ethics is unnecessary — it amounts to nothing more than virtue signaling. If we can’t realistically expect complete organic adoption, then the code is a mechanism to coerce those who disagree with it, to censure people who don’t abide by it. Those two routes — wholesale freewill adoption or coercion — are the only two ways a code of ethics can actually mean anything.

Ed. note: Wheeler uses the phrase “virtue signaling” in this essay, a phrase that I think of the real-world equivalent of “Hail Hydra”: as a way for villains identify themselves to their comrades.

Ethics is not a solvable problem but it is a manageable risk. No set of principles, not even a robust legal and regulatory infrastructure, will ensure ethical outcomes. Our goal should be to ensure that algorithm design decisions are made by competent, ethical individuals — preferably, by groups of such individuals. If we improve competency, we improve ethics. Most ethical mistakes come from the inability to foresee consequences, not the inability to tell right from wrong.

An effective ethical code doesn’t need to — in fact, probably shouldn’t — focus on ethical issues. What matters most are the consequences, not the tools we use to bring those consequences about. As long as an ethical code stipulates ways individual practitioners can prove their competence by voluntarily taking on “unnecessary” costs and risks, it will weed out the less competent and the less ethical. That’s the list we should be building. That’s the product that will result in a more ethical profession.

Joel Grus’ and Andrew K. Musselman’s podcast, Adversarial Learning, have Schaun Wheeler as a guest to talk about his stance on the proposed code of ethics. When listening to this episode, I couldn’t shake the feeling that I was listening to three smug white guys with (sometimes literally) no skin in the game.

A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life — and threaten to rip apart our social fabric

We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.

But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.

deon is a command line tool that allows you to easily add an ethics checklist to your data science projects. We support creating a new, standalone checklist file or appending a checklist to an existing analysis in many common formats.

The conversation about ethics in data science, machine learning, and AI is increasingly important. The goal of deon is to push that conversation forward and provide concrete, actionable reminders to the developers that have influence over how data science gets done.

Here are the first two sections of the default checklist that deon generates:

Data Science Ethics Checklist

A. Data Collection

A.1 Informed consent: If there are human subjects, have they given informed consent, where subjects affirmatively opt-in and have a clear understanding of the data uses to which they consent?

A.2 Collection bias: Have we considered sources of bias that could be introduced during data collection and survey design and taken steps to mitigate those?

A.3 Limit PII exposure: Have we considered ways to minimize exposure of personally identifiable information (PII) for example through anonymization or not collecting information that isn’t relevant for analysis?

B. Data Storage

B.1 Data security: Do we have a plan to protect and secure data (e.g., encryption at rest and in transit, access controls on internal users and third parties, access logs, and up-to-date software)?

B.2 Right to be forgotten: Do we have a mechanism through which an individual can request their personal information be removed?

B.3 Data retention plan: Is there a schedule or plan to delete the data after it is no longer needed?

]]>http://www.globalnerdy.com/2018/10/31/data-science-reading-list-wednesday-october-31-2018-actually-ethics-data-science/feed/0Data science reading list for Tuesday, October 30, 2018: The sexiest job, most in-demand data skills, 4 ways the data scientist has evolved, and the null hypothesis and p-valueshttp://www.globalnerdy.com/2018/10/30/data-science-reading-list-tuesday-october-30-2018/
http://www.globalnerdy.com/2018/10/30/data-science-reading-list-tuesday-october-30-2018/#respondTue, 30 Oct 2018 04:00:34 +0000http://www.globalnerdy.com/?p=26066Is data science still among the sexiest jobs of the 21st century? It was in a 2012 Harvard Business Review article that data scientist was declared “the sexiest job of the 21st century”. Is it still true six years later? I’ll spare you the torment and give you the answer, which (naturally) appears at the […]

It was in a 2012 Harvard Business Review article that data scientist was declared “the sexiest job of the 21st century”. Is it still true six years later?

I’ll spare you the torment and give you the answer, which (naturally) appears at the end of the article:

The role of data scientists is and will remain a sexy profession for some time, partly due to its relative exclusivity, and the field of data science itself will no doubt remain an exciting space.

You may find the middle of the article a little more useful, as it lists qualities of good data scientists:

A good data scientist should be:

Adaptable: Data scientists must be willing to constantly upskill themselves to master advanced machine learning skills such as deep learning. While technical skills are fundamental for data scientists, it’s crucial for them to master communication skills too so they can easily interact with domain experts or business developers. Data scientists will need to develop a better understanding of the overarching business strategy and business challenges in real-world scenarios to create solutions for real problems.

Statistics at the heart: Data scientists must have quantitative capabilities to figure out multifaceted trends within a data set that may entail more than one million rows.

Detail-oriented: Data often have errors and discrepancies, and data scientists must identify and correct incomplete, incorrect or inaccurate data. It’s critical that data are clean, high-quality and unbiased to ensure the best output upon which to make business decisions.

Good programming skills: Programming skills, together with statistics, are critical. For statistical analysis to happen, data scientists need to know programming languages (such as Java, SQL, and Python) to break down the data set in more digestible formats.

Business knowledge: While it is important for data scientists to be technically capable, they must also be business savvy and understand the organisation’s business goals and objectives, so they can analyse the data to support business success.

Based on the results of these analyses, here are some general recommendations for current and aspiring data scientists concerned with making themselves widely marketable.

Demonstrate you can do data analysis and focus on becoming really skilled at machine learning.

Invest in your communication skills. I recommend reading the book Made to Stick to help your ideas have more impact. Also check out the Hemmingway Editor app to improve the clarity of your writing.

Master a deep learning framework. Being proficient with a deep learning framework is a larger and larger part of being proficient with machine learning. For a comparison of deep learning frameworks in terms of usage, interest, and popularity see my article here.

If you are choosing between learning Python and R, choose Python. If you have Python down cold, consider learning R. You’ll definitely be more marketable if you also know R.

1. Data science is more applied than ever. What can be built and fit over a real-life scenario has the dreadful requirement of mattering. Modeling for modeling sake is no longer a thing, and best-fit diagnostics are less important than best-fit for the situation. If a model goes unused, it serves no purpose. We can no longer tolerate or afford the luxury of building models purely for R&D purposes without consideration of utilization.

2. The skill of computer use seems to have taken over the knowledge of applied statistics. Understanding the interior workings of the black box has become less important, unless you are the creator of the black box. Fewer data scientists with truly deep knowledge of statistical methods are kept in the lab creating the black boxes that hopefully get integrated within tools. This is somewhat frustrating for long time data professionals with rigorous statistical background and understanding, but this path may be necessary to truly scale modeling efforts with the volume of data, business questions, and complexities we now must answer.

3. Data scientists are not weird anymore. We’re seen as strategic inputs to the decision-making process, and our craft is becoming much more understood. This trend is evidenced by C-level positions at large companies, vertical alignment and paths for data scientists, and inclusion at the highest levels, as well as the many academic programs and emphasis now available globally. This appreciation and positioning can sometimes make the field appealing for what seasoned data scientists might call the “wrong reasons” such as corporate fame and value. I would argue that we really want professionals in the field with a thirst for the truth – the science should be about empirically answering questions, and powered by truth-seekers at their heart.

4. Data Science is becoming more widely recognized as both art and science. Understanding the importance of the human – machine integration and complementary decision-making skills from each appears to have made its way more squarely into our field of understanding.

]]>http://www.globalnerdy.com/2018/10/30/data-science-reading-list-tuesday-october-30-2018/feed/0AI hasn’t brought about the Singularity, but it’s given us the next best thinghttp://www.globalnerdy.com/2018/10/29/ai-hasnt-brought-singularity-given-us-next-best-thing/
http://www.globalnerdy.com/2018/10/29/ai-hasnt-brought-singularity-given-us-next-best-thing/#respondMon, 29 Oct 2018 20:59:18 +0000http://www.globalnerdy.com/?p=26059And (surprise, surprise), it’s open source and on Github.

]]>http://www.globalnerdy.com/2018/10/29/ai-hasnt-brought-singularity-given-us-next-best-thing/feed/0Tech t-shirt of the dayhttp://www.globalnerdy.com/2018/10/29/tech-t-shirt-day/
http://www.globalnerdy.com/2018/10/29/tech-t-shirt-day/#respondMon, 29 Oct 2018 13:43:13 +0000http://www.globalnerdy.com/?p=26056Click the photo to see it at full size. Thanks to the Facebook group People of Color in Tech for the find!

]]>http://www.globalnerdy.com/2018/10/29/tech-t-shirt-day/feed/0Data science reading list for Monday, October 29, 2018: The worst data science article, 5 basic stats concepts you need to know, Bayes, democratization, and web scrapinghttp://www.globalnerdy.com/2018/10/29/data-science-reading-list-monday-october-29-2018/
http://www.globalnerdy.com/2018/10/29/data-science-reading-list-monday-october-29-2018/#respondMon, 29 Oct 2018 04:00:17 +0000http://www.globalnerdy.com/?p=26028A terrible “data skills” article that you should read, but only as a warning I remember the hype that surrounded the web in the late 1990s. I also remember the copious amount of well-intentioned misinformation that made the rounds as writers attempted to capitalize on that hype. It’s now data science’s turn, if this bit […]

I remember the hype that surrounded the web in the late 1990s. I also remember the copious amount of well-intentioned misinformation that made the rounds as writers attempted to capitalize on that hype. It’s now data science’s turn, if this bit of “advertorial” in Harvard Business Review — Prioritize Which Data Skills Your Company Needs with This 2×2 Matrix — is any indication.

Written by Chris Littlewood, chief innovation and product officer of filtered.com (I’m not going to help them by linking to their site), a company that purports to use AI to “lift productivity by making learning recommendations”, the article clearly highlight’s the author’s ignorance and HBR’s willingness to publish any article that has to do with data or data science. To the credit of the readers, a number of them registered with the site simply to be able to post comments pointing out how nonsensical the article was.

Treat this article as an object lesson in technology hype, as well a sign that data science skills are seen as valuable.

Forget that the article mentioned above said that mathematics and statistics aren’t useful data skills — you can’t do data science without them! You’ll need to understand these 5 concepts (in addition to others):

One of the better data science podcasts out there is Kyle Polich’s Data Skeptic, which has been around since 2014 and has over 400 episodes. The podcast features short mini-episodes explaining high level concepts in data science, and longer interview segments with researchers and practitioners.

I’ve just started working my way through this podcast, and have used the example in episode 5, Bayesian Updating, to explain Bayes’ Theorem to people who avoiding studying probability and stats. Give it a listen, then check out the rest of the podcast episodes!

Intelligent people find new uses for data science every day. Still, despite the explosion of interest in the data collected by just about every sector of American business — from financial companies and health care firms to management consultancies and the government — many organizations continue to relegate data-science knowledge to a small number of employees.

That’s a mistake — and in the long run, it’s unsustainable. Think of it this way: Very few companies expect only professional writers to know how to write. So why ask only professional data scientists to understand and analyze data, at least at a basic level?

One of the first tasks that I was given in my job as a Data Scientist involved Web Scraping. This was a completely alien concept to me at the time, gathering data from websites using code, but is one of the most logical and easily accessible sources of data. After a few attempts, web scraping has become second nature to me and one of the many skills that I use almost daily.

In this tutorial I will go through a simple example of how to scrape a website to gather data on the top 100 companies in 2018 from Fast Track. Automating this process with a web scraper avoids manual data gathering, saves time and also allows you to have all the data on the companies in one structured file.

Tuesday, October 30

In the last Tampa iOS Meetup, we built a simple app that could analyze a photo and say if it was a picture of healthy or unhealthy food. It used a CoreML model that someone trained by giving it hundreds of photos of food and classifying each as healthy (mostly photos of fruit) and unhealthy (photos of pastries, cookies, and other sugary foods).

Someone asked “How do make our own models?” at the end of that meetup. In this meetup, we’ll answer that question by going through the steps to build our own model using Python and Turi Create, and then incorporating that model into a Core ML app.

If you’ve always wanted to get into machine learning, but found the material on it intimidating, you’ll want to join us at this friendly meetup, where you’ll get than hang of machine learning, iOS-style!

]]>http://www.globalnerdy.com/2018/10/26/whats-happening-tampa-bay-tech-entrepreneur-nerd-scene-week-monday-october-29-2018/feed/0What’s happening in the Tampa Bay tech/entrepreneur/nerd scene (Week of Monday, October 22, 2018)http://www.globalnerdy.com/2018/10/20/whats-happening-tampa-bay-tech-entrepreneur-nerd-scene-week-monday-october-22-2018/
http://www.globalnerdy.com/2018/10/20/whats-happening-tampa-bay-tech-entrepreneur-nerd-scene-week-monday-october-22-2018/#respondSun, 21 Oct 2018 01:39:05 +0000http://www.globalnerdy.com/?p=26011Every week, I compile a list of events for developers, technologists, tech entrepreneurs, and nerds in and around the Tampa Bay area. We’ve got a lot of events going on this week, and here they are! Monday, October 22 Startup Space Tampa Bay Entrepreneurs and Small Businesses — How to Set Up a Business Without […]

Thursday, October 25

The Mainframe continues Tampa’s first interactive event series designed to immerse black tech entrepreneurs, innovators, professionals, technologists and enthusiast in the Bay Area’s local start up ecosystem.

This quarterly after-work event connects you with Tampa’s most notable innovators and business leaders over drinks and appetizers. While getting a sneak peek at new products and an opportunity to share your area of expertise.